arXiv:2608. 06221v1 Announce Type: cross Abstract: Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot.
By Alperen Kenan, Paul Bremner, Manuel Giuliani
arXiv:2609.34182v2 Announce Type: replace-cross
Abstract: Dexterous manipulation requires tactile feedback. However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleope...
By Wenqiao Li, Qianyou Zhao, Jiawen Hao, Xuezhou Zhu, Tengyu Liu, Kaifeng Zhang, Chuan Wen, Siyuan Huang
arXiv:2609.24660v2 Announce Type: cross
Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot...
By Shengcheng Luo, Xiaoyang Cheng, Hong Ying, Xiaoying Zhou, Jiaming Jiang, Haoran Guo, Wanlin Li, Ziyuan Jiao, Chenxi Xiao
EgoSpeedUp is a framework that transfers human manipulation tempo to robot policies by aligning and retiming robot demonstrations using phase-wise tempo estimates derived from human demonstrations. The method improves task success rates by an average of 25 percentage points and reduces successful execution time by 36.5% on two real-world manipulation tasks. It demonstrates that human manipulation tempo can serve as an effective temporal reference for faster and more reliable robot policies.
By Hanbit Oh, Yukiyasu Domae, Takuma Yagi
arXiv:2608. 14028v1 Announce Type: cross Abstract: Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spaces vary across embodiments.
By Zhiyue Zhao, Jingyi Wu, Hairuo Liu, Mingyu Liu, Liyang Li, Hengdi Zhang, Tong He, Zhengxue Cheng
arXiv:2506. 20668v3 Announce Type: replace-cross Abstract: We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or paired human-robot data.
By Sungjae Park, Homanga Bharadhwaj, Shubham Tulsiani